{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd \n",
    "def load_housing_data(houseing_path='./'):\n",
    "    csv_path = os.path.join(houseing_path,'housing.csv')\n",
    "    return pd.read_csv(csv_path)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing  =load_housing_data()\n",
    "houseing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "houseing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing['ocean_proximity'].value_counts()  # 查看 有多少种分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing.describe()  # 显示 数值属性的 摘要 ， 不包括字符串类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "houseing.hist(bins = 50,figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如果图中的最大值 或者 最小值 为一条直线， 则 被设置了上限\n",
    "#解决方法 \n",
    "# 问上游要信息\n",
    "#删除上限值\n",
    "##### 重尾  头高尾长\n",
    "  ### 转化数据，把数据的形状转变成 偏钟型分布（正态分布，平均值为0，标准差为1）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建训练集和测试集\n",
    "   *训练集主要用于模型的训练，占所有数据的80%，剩下的为测试集 占20%\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data,test_radio):\n",
    "    np.random.seed(20)\n",
    "    indices = np.random.permutation(len(data))  #打乱原来的元素顺序,给的数据为一个整数，对一个整数进行打乱顺序，比如 给5 结果为1-5 打乱顺序\n",
    "    test_set_size =int( len(data) * test_radio)\n",
    "    test_indices = indices[:test_set_size]\n",
    "    train_indices = indices[test_set_size:]\n",
    "    return data.iloc[train_indices],data.iloc[test_indices]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set = split_train_test(houseing,0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14448"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6192"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对样本设置唯一 的标识符， 对标识符 取哈希值 ， 取哈希值的最后一个字节，这个值小于等于51 （256的20%） 加入到大数据里去\n",
    "import hashlib\n",
    "def test_set_check(identifier,test_radio,hash = hashlib.md5):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256*test_radio  #返回摘要，把哈希值对象 ，作为二进制数据字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data,test_radio,id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_:test_set_check(id_,test_radio))\n",
    "    return data.loc[~in_test_set],data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "houseing_id_index = houseing.reset_index()   #使用行索引作为标识符ID\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "4        NEAR BAY  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_id_index.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set = split_train_test_by_id(houseing_id_index,0.2,'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>352100.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
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       "      <td>20634</td>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.27</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2332.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>3.7125</td>\n",
       "      <td>116800.0</td>\n",
       "      <td>INLAND</td>\n",
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       "    <tr>\n",
       "      <th>20635</th>\n",
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       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>INLAND</td>\n",
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       "      <th>20636</th>\n",
       "      <td>20636</td>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>INLAND</td>\n",
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       "      <td>39.43</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>349.0</td>\n",
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       "      <td>616.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "      <td>INLAND</td>\n",
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       "<p>16362 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "       index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0          0    -122.23     37.88                41.0        880.0   \n",
       "1          1    -122.22     37.86                21.0       7099.0   \n",
       "2          2    -122.24     37.85                52.0       1467.0   \n",
       "3          3    -122.25     37.85                52.0       1274.0   \n",
       "6          6    -122.25     37.84                52.0       2535.0   \n",
       "...      ...        ...       ...                 ...          ...   \n",
       "20634  20634    -121.56     39.27                28.0       2332.0   \n",
       "20635  20635    -121.09     39.48                25.0       1665.0   \n",
       "20636  20636    -121.21     39.49                18.0        697.0   \n",
       "20638  20638    -121.32     39.43                18.0       1860.0   \n",
       "20639  20639    -121.24     39.37                16.0       2785.0   \n",
       "\n",
       "       total_bedrooms  population  households  median_income  \\\n",
       "0               129.0       322.0       126.0         8.3252   \n",
       "1              1106.0      2401.0      1138.0         8.3014   \n",
       "2               190.0       496.0       177.0         7.2574   \n",
       "3               235.0       558.0       219.0         5.6431   \n",
       "6               489.0      1094.0       514.0         3.6591   \n",
       "...               ...         ...         ...            ...   \n",
       "20634           395.0      1041.0       344.0         3.7125   \n",
       "20635           374.0       845.0       330.0         1.5603   \n",
       "20636           150.0       356.0       114.0         2.5568   \n",
       "20638           409.0       741.0       349.0         1.8672   \n",
       "20639           616.0      1387.0       530.0         2.3886   \n",
       "\n",
       "       median_house_value ocean_proximity  \n",
       "0                452600.0        NEAR BAY  \n",
       "1                358500.0        NEAR BAY  \n",
       "2                352100.0        NEAR BAY  \n",
       "3                341300.0        NEAR BAY  \n",
       "6                299200.0        NEAR BAY  \n",
       "...                   ...             ...  \n",
       "20634            116800.0          INLAND  \n",
       "20635             78100.0          INLAND  \n",
       "20636             77100.0          INLAND  \n",
       "20638             84700.0          INLAND  \n",
       "20639             89400.0          INLAND  \n",
       "\n",
       "[16362 rows x 11 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "train_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>40.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.3578</td>\n",
       "      <td>147500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2.1806</td>\n",
       "      <td>99700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20619</th>\n",
       "      <td>20619</td>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.01</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>2.7303</td>\n",
       "      <td>99100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20625</th>\n",
       "      <td>20625</td>\n",
       "      <td>-121.52</td>\n",
       "      <td>39.12</td>\n",
       "      <td>37.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.1250</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20632</th>\n",
       "      <td>20632</td>\n",
       "      <td>-121.45</td>\n",
       "      <td>39.26</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>115600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20633</th>\n",
       "      <td>20633</td>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.19</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>2.5495</td>\n",
       "      <td>98300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>20637</td>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4278 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "4          4    -122.25     37.85                52.0       1627.0   \n",
       "5          5    -122.25     37.85                52.0        919.0   \n",
       "11        11    -122.26     37.85                52.0       3503.0   \n",
       "20        20    -122.27     37.85                40.0        751.0   \n",
       "23        23    -122.27     37.84                52.0       1688.0   \n",
       "...      ...        ...       ...                 ...          ...   \n",
       "20619  20619    -121.56     39.01                22.0       1891.0   \n",
       "20625  20625    -121.52     39.12                37.0        102.0   \n",
       "20632  20632    -121.45     39.26                15.0       2319.0   \n",
       "20633  20633    -121.53     39.19                27.0       2080.0   \n",
       "20637  20637    -121.22     39.43                17.0       2254.0   \n",
       "\n",
       "       total_bedrooms  population  households  median_income  \\\n",
       "4               280.0       565.0       259.0         3.8462   \n",
       "5               213.0       413.0       193.0         4.0368   \n",
       "11              752.0      1504.0       734.0         3.2705   \n",
       "20              184.0       409.0       166.0         1.3578   \n",
       "23              337.0       853.0       325.0         2.1806   \n",
       "...               ...         ...         ...            ...   \n",
       "20619           340.0      1023.0       296.0         2.7303   \n",
       "20625            17.0        29.0        14.0         4.1250   \n",
       "20632           416.0      1047.0       385.0         3.1250   \n",
       "20633           412.0      1082.0       382.0         2.5495   \n",
       "20637           485.0      1007.0       433.0         1.7000   \n",
       "\n",
       "       median_house_value ocean_proximity  \n",
       "4                342200.0        NEAR BAY  \n",
       "5                269700.0        NEAR BAY  \n",
       "11               241800.0        NEAR BAY  \n",
       "20               147500.0        NEAR BAY  \n",
       "23                99700.0        NEAR BAY  \n",
       "...                   ...             ...  \n",
       "20619             99100.0          INLAND  \n",
       "20625             72000.0          INLAND  \n",
       "20632            115600.0          INLAND  \n",
       "20633             98300.0          INLAND  \n",
       "20637             92300.0          INLAND  \n",
       "\n",
       "[4278 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 基于行索引，不能中间和末尾插入数据， 不然数据会变\n",
    "#寻找稳定特征来创建唯一标识\n",
    "houseing_id_index['id'] = houseing_id_index['longitude']*1000 +houseing['latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_id_index.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set = split_train_test_by_id(houseing_id_index,0.2,'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set,test_set = train_test_split(houseing,test_size = 0.2,random_state = 111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12687</th>\n",
       "      <td>-121.50</td>\n",
       "      <td>38.59</td>\n",
       "      <td>43.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.7250</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17088</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.47</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7740.0</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>4124.0</td>\n",
       "      <td>1743.0</td>\n",
       "      <td>3.3268</td>\n",
       "      <td>322800.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7321</th>\n",
       "      <td>-118.18</td>\n",
       "      <td>33.99</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>1.6341</td>\n",
       "      <td>161800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12595</th>\n",
       "      <td>-121.49</td>\n",
       "      <td>38.54</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1655.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>841.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1.6932</td>\n",
       "      <td>78400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14624</th>\n",
       "      <td>-117.17</td>\n",
       "      <td>32.77</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>3.7670</td>\n",
       "      <td>166800.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7443</th>\n",
       "      <td>-118.19</td>\n",
       "      <td>33.96</td>\n",
       "      <td>40.0</td>\n",
       "      <td>979.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>2.6354</td>\n",
       "      <td>151800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4182</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>34.13</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>4.6528</td>\n",
       "      <td>244400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4820</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>39.0</td>\n",
       "      <td>993.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>1.2786</td>\n",
       "      <td>121900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10196</th>\n",
       "      <td>-117.94</td>\n",
       "      <td>33.86</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>875.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>4.6964</td>\n",
       "      <td>183100.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12116</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.96</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9179.0</td>\n",
       "      <td>1361.0</td>\n",
       "      <td>4573.0</td>\n",
       "      <td>1294.0</td>\n",
       "      <td>5.2530</td>\n",
       "      <td>163300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "12687    -121.50     38.59                43.0         88.0            21.0   \n",
       "17088    -122.22     37.47                23.0       7740.0          1943.0   \n",
       "7321     -118.18     33.99                38.0       1010.0           315.0   \n",
       "12595    -121.49     38.54                37.0       1655.0           393.0   \n",
       "14624    -117.17     32.77                35.0       1399.0           274.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "7443     -118.19     33.96                40.0        979.0           296.0   \n",
       "4182     -118.23     34.13                47.0       1162.0           235.0   \n",
       "4820     -118.30     34.05                39.0        993.0           506.0   \n",
       "10196    -117.94     33.86                35.0       1235.0           227.0   \n",
       "12116    -117.23     33.96                 5.0       9179.0          1361.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "12687       119.0        19.0         1.7250             67500.0   \n",
       "17088      4124.0      1743.0         3.3268            322800.0   \n",
       "7321       1157.0       301.0         1.6341            161800.0   \n",
       "12595       841.0       355.0         1.6932             78400.0   \n",
       "14624       695.0       281.0         3.7670            166800.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "7443        934.0       292.0         2.6354            151800.0   \n",
       "4182        781.0       268.0         4.6528            244400.0   \n",
       "4820       1765.0       464.0         1.2786            121900.0   \n",
       "10196       875.0       220.0         4.6964            183100.0   \n",
       "12116      4573.0      1294.0         5.2530            163300.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "12687          INLAND  \n",
       "17088      NEAR OCEAN  \n",
       "7321        <1H OCEAN  \n",
       "12595          INLAND  \n",
       "14624      NEAR OCEAN  \n",
       "...               ...  \n",
       "7443        <1H OCEAN  \n",
       "4182        <1H OCEAN  \n",
       "4820        <1H OCEAN  \n",
       "10196       <1H OCEAN  \n",
       "12116          INLAND  \n",
       "\n",
       "[16512 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据可视化 \n",
    "### 通过图表的形式 观察数据，并得出相对于的结论"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "houseing['median_income'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#     1.创建收入类别的属性\n",
    "#     2.分层，不应该分层太多，但是每一层要有足够的数据量\n",
    "#     3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建1.5倍的中位数\n",
    "houseing['income_cat'] = np.ceil(houseing['median_income']/1.5)\n",
    "houseing['income_cat'].head(20)\n",
    "houseing['income_cat'].where(houseing['income_cat']<5,5.0,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     5.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     3.0\n",
       "7     3.0\n",
       "8     2.0\n",
       "9     3.0\n",
       "10    3.0\n",
       "11    3.0\n",
       "12    3.0\n",
       "13    2.0\n",
       "14    2.0\n",
       "15    2.0\n",
       "16    2.0\n",
       "17    2.0\n",
       "18    2.0\n",
       "19    2.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把连续值转化成标签\n",
    "houseing['income_cat'] = pd.cut(houseing['income_cat'],bins=[0.,1.5,3.0,4.5,np.inf],labels=[1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     4\n",
       "2     4\n",
       "3     3\n",
       "4     2\n",
       "5     2\n",
       "6     2\n",
       "7     2\n",
       "8     2\n",
       "9     2\n",
       "10    2\n",
       "11    2\n",
       "12    2\n",
       "13    2\n",
       "14    2\n",
       "15    2\n",
       "16    2\n",
       "17    2\n",
       "18    2\n",
       "19    2\n",
       "Name: income_cat, dtype: category\n",
       "Categories (4, int64): [1 < 2 < 3 < 4]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    13817\n",
       "3     3639\n",
       "4     2362\n",
       "1      822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing['income_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 收入类别直方图\n",
    "houseing['income_cat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "s = StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for a,b  in s.split(houseing,houseing['income_cat']):\n",
    "    start_train_set = houseing.loc[a]\n",
    "    start_test_set = houseing.loc[b]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 11)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17098</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.46</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6841.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2681.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>7.1088</td>\n",
       "      <td>443300.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.17</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1784.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>966.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>3.6094</td>\n",
       "      <td>261500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10016</th>\n",
       "      <td>-121.18</td>\n",
       "      <td>39.23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>3.7125</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>-116.99</td>\n",
       "      <td>32.72</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1112.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>4.7679</td>\n",
       "      <td>169500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8018</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.84</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2572.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>5.1836</td>\n",
       "      <td>219700.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7655</th>\n",
       "      <td>-118.27</td>\n",
       "      <td>33.82</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1642.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>4.1292</td>\n",
       "      <td>201900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>-122.11</td>\n",
       "      <td>37.70</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1689.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>2.1552</td>\n",
       "      <td>161400.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.87</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2773.0</td>\n",
       "      <td>998.0</td>\n",
       "      <td>1721.0</td>\n",
       "      <td>949.0</td>\n",
       "      <td>1.1859</td>\n",
       "      <td>241700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7395</th>\n",
       "      <td>-118.25</td>\n",
       "      <td>33.95</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1576.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>1252.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>1.9798</td>\n",
       "      <td>98100.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11758</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>38.75</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9137.0</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>3667.0</td>\n",
       "      <td>1294.0</td>\n",
       "      <td>5.4896</td>\n",
       "      <td>229600.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7478</th>\n",
       "      <td>-118.21</td>\n",
       "      <td>33.93</td>\n",
       "      <td>39.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2.7679</td>\n",
       "      <td>108900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16344</th>\n",
       "      <td>-121.33</td>\n",
       "      <td>38.04</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1421.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>5.1878</td>\n",
       "      <td>153900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12671</th>\n",
       "      <td>-121.42</td>\n",
       "      <td>38.49</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13180.0</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>7235.0</td>\n",
       "      <td>2335.0</td>\n",
       "      <td>3.3630</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12680</th>\n",
       "      <td>-121.39</td>\n",
       "      <td>38.55</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>1053.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>3.5278</td>\n",
       "      <td>126200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17872</th>\n",
       "      <td>-121.96</td>\n",
       "      <td>37.43</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2514.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>2205.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>3.3859</td>\n",
       "      <td>158000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3350</th>\n",
       "      <td>-120.77</td>\n",
       "      <td>40.65</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2635.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1.7214</td>\n",
       "      <td>118300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17938</th>\n",
       "      <td>-121.94</td>\n",
       "      <td>37.34</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3377.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>1674.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>3.4233</td>\n",
       "      <td>229300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13346</th>\n",
       "      <td>-117.64</td>\n",
       "      <td>34.03</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2050.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>1044.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>4.8281</td>\n",
       "      <td>137000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19172</th>\n",
       "      <td>-122.67</td>\n",
       "      <td>38.44</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2551.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>4.3587</td>\n",
       "      <td>196400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>-120.66</td>\n",
       "      <td>40.42</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>717.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>2.5074</td>\n",
       "      <td>66400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17098    -122.25     37.46                33.0       6841.0           950.0   \n",
       "6622     -118.13     34.17                52.0       1784.0           368.0   \n",
       "10016    -121.18     39.23                 8.0       2112.0           360.0   \n",
       "14984    -116.99     32.72                11.0       1112.0           164.0   \n",
       "8018     -118.10     33.84                36.0       2572.0           421.0   \n",
       "7655     -118.27     33.82                28.0       1642.0           434.0   \n",
       "693      -122.11     37.70                23.0       1689.0           461.0   \n",
       "461      -122.26     37.87                52.0       2773.0           998.0   \n",
       "7395     -118.25     33.95                41.0       1576.0           339.0   \n",
       "11758    -121.24     38.75                 5.0       9137.0          1368.0   \n",
       "7478     -118.21     33.93                39.0        354.0            73.0   \n",
       "16344    -121.33     38.04                10.0       1421.0           204.0   \n",
       "12671    -121.42     38.49                17.0      13180.0          2444.0   \n",
       "12680    -121.39     38.55                25.0       2171.0           431.0   \n",
       "17872    -121.96     37.43                18.0       2514.0           578.0   \n",
       "3350     -120.77     40.65                11.0       2635.0           667.0   \n",
       "17938    -121.94     37.34                29.0       3377.0           853.0   \n",
       "13346    -117.64     34.03                11.0       2050.0           382.0   \n",
       "19172    -122.67     38.44                29.0       2551.0           448.0   \n",
       "3356     -120.66     40.42                35.0       1450.0           325.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17098      2681.0       980.0         7.1088            443300.0   \n",
       "6622        966.0       345.0         3.6094            261500.0   \n",
       "10016       782.0       344.0         3.7125            175000.0   \n",
       "14984       441.0       174.0         4.7679            169500.0   \n",
       "8018       1318.0       434.0         5.1836            219700.0   \n",
       "7655       1575.0       420.0         4.1292            201900.0   \n",
       "693         828.0       443.0         2.1552            161400.0   \n",
       "461        1721.0       949.0         1.1859            241700.0   \n",
       "7395       1252.0       302.0         1.9798             98100.0   \n",
       "11758      3667.0      1294.0         5.4896            229600.0   \n",
       "7478        184.0        58.0         2.7679            108900.0   \n",
       "16344       657.0       209.0         5.1878            153900.0   \n",
       "12671      7235.0      2335.0         3.3630            103000.0   \n",
       "12680      1053.0       422.0         3.5278            126200.0   \n",
       "17872      2205.0       545.0         3.3859            158000.0   \n",
       "3350        280.0       132.0         1.7214            118300.0   \n",
       "17938      1674.0       792.0         3.4233            229300.0   \n",
       "13346      1044.0       371.0         4.8281            137000.0   \n",
       "19172      1165.0       456.0         4.3587            196400.0   \n",
       "3356        717.0       297.0         2.5074             66400.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17098      NEAR OCEAN          4  \n",
       "6622        <1H OCEAN          2  \n",
       "10016          INLAND          2  \n",
       "14984       <1H OCEAN          3  \n",
       "8018        <1H OCEAN          3  \n",
       "7655        <1H OCEAN          2  \n",
       "693          NEAR BAY          2  \n",
       "461          NEAR BAY          1  \n",
       "7395        <1H OCEAN          2  \n",
       "11758          INLAND          3  \n",
       "7478        <1H OCEAN          2  \n",
       "16344          INLAND          3  \n",
       "12671          INLAND          2  \n",
       "12680          INLAND          2  \n",
       "17872       <1H OCEAN          2  \n",
       "3350           INLAND          2  \n",
       "17938       <1H OCEAN          2  \n",
       "13346          INLAND          3  \n",
       "19172       <1H OCEAN          2  \n",
       "3356           INLAND          2  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.669331\n",
       "3    0.176357\n",
       "4    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_test_set['income_cat'].value_counts()/len(start_test_set)   #抽样数据集合的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.669428\n",
       "3    0.176308\n",
       "4    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing['income_cat'].value_counts()/len(houseing)   #完整数据集合的比例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 将地理数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立一个各区域的分布图，以便于数据可视化\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "houseing.plot(kind ='scatter',x= 'longitude',y='latitude',alpha =0.4,s=houseing['population']/100,label=\"populations\",figsize=(10,7),\n",
    "             c=\"median_house_value\",cmap=plt.get_cmap(\"jet\"),colorbar = True,sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Latitude')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg   #加载图片\n",
    "california_img = mpimg.imread('./california.png')   #加载图片\n",
    "houseing.plot(kind ='scatter',x= 'longitude',y='latitude',alpha =0.4,s=houseing['population']/100,label=\"populations\",figsize=(10,7),\n",
    "             c=\"median_house_value\",cmap=plt.get_cmap(\"jet\"),colorbar = True,sharex=False)\n",
    "\n",
    "\n",
    "plt.imshow(california_img,extent=[-124.55,-113.80,32.45,42.05],alpha=0.5,cmap=plt.get_cmap(\"jet\")) #把图片对应到图中\n",
    "plt.xlabel('Longitude',fontsize=14)\n",
    "plt.ylabel('Latitude',fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
       "population                0.877747    1.000000    0.907222       0.004834   \n",
       "households                0.979728    0.907222    1.000000       0.013033   \n",
       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.045967  \n",
       "latitude                     -0.144160  \n",
       "housing_median_age            0.105623  \n",
       "total_rooms                   0.134153  \n",
       "total_bedrooms                0.049686  \n",
       "population                   -0.024650  \n",
       "households                    0.065843  \n",
       "median_income                 0.688075  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_corr = houseing.corr()   #corr（） 方法用于寻找特征值   0表示两个数毫不相关 1表示两个数正相关 -1表示两个数负相关\n",
    "houseing_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_corr['median_house_value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='median_house_value', ylabel='median_house_value'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='median_house_value'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='median_house_value'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='median_house_value'>],\n",
       "       [<AxesSubplot:xlabel='median_house_value', ylabel='median_income'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='median_income'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='median_income'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='median_income'>],\n",
       "       [<AxesSubplot:xlabel='median_house_value', ylabel='total_rooms'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='total_rooms'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='total_rooms'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='total_rooms'>],\n",
       "       [<AxesSubplot:xlabel='median_house_value', ylabel='housing_median_age'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='housing_median_age'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='housing_median_age'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='housing_median_age'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attribute = ['median_house_value','median_income','total_rooms','housing_median_age']\n",
    "scatter_matrix(houseing[attribute],figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 16.0, 0.0, 550000.0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "houseing.plot(kind = 'scatter',x='median_income',y='median_house_value',alpha=0.1)\n",
    "plt.axis([0,16,0,550000])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 上图可知 50w被设置了上限，还有几条横线 也可能是异常值，可以在后期把数据删除掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置合成特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "houseing['room_household'] = houseing['total_rooms'] /houseing['households']\n",
    "houseing['bedrooms_per_rooms'] = houseing['total_bedrooms'] /houseing['total_rooms']\n",
    "houseing['population_per_households'] = houseing['population'] /houseing['households']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>room_household</th>\n",
       "      <th>bedrooms_per_rooms</th>\n",
       "      <th>population_per_households</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.027540</td>\n",
       "      <td>0.092657</td>\n",
       "      <td>0.002476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.106389</td>\n",
       "      <td>-0.113815</td>\n",
       "      <td>0.002366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>-0.153277</td>\n",
       "      <td>0.136089</td>\n",
       "      <td>0.013191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.133798</td>\n",
       "      <td>-0.187900</td>\n",
       "      <td>-0.024581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>0.001538</td>\n",
       "      <td>0.084238</td>\n",
       "      <td>-0.028355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>-0.072213</td>\n",
       "      <td>0.035319</td>\n",
       "      <td>0.069863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>-0.080598</td>\n",
       "      <td>0.065087</td>\n",
       "      <td>-0.027309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>0.326895</td>\n",
       "      <td>-0.615661</td>\n",
       "      <td>0.018766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151948</td>\n",
       "      <td>-0.255880</td>\n",
       "      <td>-0.023737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_household</th>\n",
       "      <td>-0.027540</td>\n",
       "      <td>0.106389</td>\n",
       "      <td>-0.153277</td>\n",
       "      <td>0.133798</td>\n",
       "      <td>0.001538</td>\n",
       "      <td>-0.072213</td>\n",
       "      <td>-0.080598</td>\n",
       "      <td>0.326895</td>\n",
       "      <td>0.151948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.416952</td>\n",
       "      <td>-0.004852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms_per_rooms</th>\n",
       "      <td>0.092657</td>\n",
       "      <td>-0.113815</td>\n",
       "      <td>0.136089</td>\n",
       "      <td>-0.187900</td>\n",
       "      <td>0.084238</td>\n",
       "      <td>0.035319</td>\n",
       "      <td>0.065087</td>\n",
       "      <td>-0.615661</td>\n",
       "      <td>-0.255880</td>\n",
       "      <td>-0.416952</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_per_households</th>\n",
       "      <td>0.002476</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.013191</td>\n",
       "      <td>-0.024581</td>\n",
       "      <td>-0.028355</td>\n",
       "      <td>0.069863</td>\n",
       "      <td>-0.027309</td>\n",
       "      <td>0.018766</td>\n",
       "      <td>-0.023737</td>\n",
       "      <td>-0.004852</td>\n",
       "      <td>0.002938</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           longitude  latitude  housing_median_age  \\\n",
       "longitude                   1.000000 -0.924664           -0.108197   \n",
       "latitude                   -0.924664  1.000000            0.011173   \n",
       "housing_median_age         -0.108197  0.011173            1.000000   \n",
       "total_rooms                 0.044568 -0.036100           -0.361262   \n",
       "total_bedrooms              0.069608 -0.066983           -0.320451   \n",
       "population                  0.099773 -0.108785           -0.296244   \n",
       "households                  0.055310 -0.071035           -0.302916   \n",
       "median_income              -0.015176 -0.079809           -0.119034   \n",
       "median_house_value         -0.045967 -0.144160            0.105623   \n",
       "room_household             -0.027540  0.106389           -0.153277   \n",
       "bedrooms_per_rooms          0.092657 -0.113815            0.136089   \n",
       "population_per_households   0.002476  0.002366            0.013191   \n",
       "\n",
       "                           total_rooms  total_bedrooms  population  \\\n",
       "longitude                     0.044568        0.069608    0.099773   \n",
       "latitude                     -0.036100       -0.066983   -0.108785   \n",
       "housing_median_age           -0.361262       -0.320451   -0.296244   \n",
       "total_rooms                   1.000000        0.930380    0.857126   \n",
       "total_bedrooms                0.930380        1.000000    0.877747   \n",
       "population                    0.857126        0.877747    1.000000   \n",
       "households                    0.918484        0.979728    0.907222   \n",
       "median_income                 0.198050       -0.007723    0.004834   \n",
       "median_house_value            0.134153        0.049686   -0.024650   \n",
       "room_household                0.133798        0.001538   -0.072213   \n",
       "bedrooms_per_rooms           -0.187900        0.084238    0.035319   \n",
       "population_per_households    -0.024581       -0.028355    0.069863   \n",
       "\n",
       "                           households  median_income  median_house_value  \\\n",
       "longitude                    0.055310      -0.015176           -0.045967   \n",
       "latitude                    -0.071035      -0.079809           -0.144160   \n",
       "housing_median_age          -0.302916      -0.119034            0.105623   \n",
       "total_rooms                  0.918484       0.198050            0.134153   \n",
       "total_bedrooms               0.979728      -0.007723            0.049686   \n",
       "population                   0.907222       0.004834           -0.024650   \n",
       "households                   1.000000       0.013033            0.065843   \n",
       "median_income                0.013033       1.000000            0.688075   \n",
       "median_house_value           0.065843       0.688075            1.000000   \n",
       "room_household              -0.080598       0.326895            0.151948   \n",
       "bedrooms_per_rooms           0.065087      -0.615661           -0.255880   \n",
       "population_per_households   -0.027309       0.018766           -0.023737   \n",
       "\n",
       "                           room_household  bedrooms_per_rooms  \\\n",
       "longitude                       -0.027540            0.092657   \n",
       "latitude                         0.106389           -0.113815   \n",
       "housing_median_age              -0.153277            0.136089   \n",
       "total_rooms                      0.133798           -0.187900   \n",
       "total_bedrooms                   0.001538            0.084238   \n",
       "population                      -0.072213            0.035319   \n",
       "households                      -0.080598            0.065087   \n",
       "median_income                    0.326895           -0.615661   \n",
       "median_house_value               0.151948           -0.255880   \n",
       "room_household                   1.000000           -0.416952   \n",
       "bedrooms_per_rooms              -0.416952            1.000000   \n",
       "population_per_households       -0.004852            0.002938   \n",
       "\n",
       "                           population_per_households  \n",
       "longitude                                   0.002476  \n",
       "latitude                                    0.002366  \n",
       "housing_median_age                          0.013191  \n",
       "total_rooms                                -0.024581  \n",
       "total_bedrooms                             -0.028355  \n",
       "population                                  0.069863  \n",
       "households                                 -0.027309  \n",
       "median_income                               0.018766  \n",
       "median_house_value                         -0.023737  \n",
       "room_household                             -0.004852  \n",
       "bedrooms_per_rooms                          0.002938  \n",
       "population_per_households                   1.000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_corr = houseing.corr()\n",
    "house_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value           1.000000\n",
       "median_income                0.688075\n",
       "room_household               0.151948\n",
       "total_rooms                  0.134153\n",
       "housing_median_age           0.105623\n",
       "households                   0.065843\n",
       "total_bedrooms               0.049686\n",
       "population_per_households   -0.023737\n",
       "population                  -0.024650\n",
       "longitude                   -0.045967\n",
       "latitude                    -0.144160\n",
       "bedrooms_per_rooms          -0.255880\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_corr['median_house_value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17098</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.46</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6841.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2681.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>7.1088</td>\n",
       "      <td>443300.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.17</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1784.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>966.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>3.6094</td>\n",
       "      <td>261500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10016</th>\n",
       "      <td>-121.18</td>\n",
       "      <td>39.23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>3.7125</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>-116.99</td>\n",
       "      <td>32.72</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1112.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>4.7679</td>\n",
       "      <td>169500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8018</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.84</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2572.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>5.1836</td>\n",
       "      <td>219700.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17098    -122.25     37.46                33.0       6841.0           950.0   \n",
       "6622     -118.13     34.17                52.0       1784.0           368.0   \n",
       "10016    -121.18     39.23                 8.0       2112.0           360.0   \n",
       "14984    -116.99     32.72                11.0       1112.0           164.0   \n",
       "8018     -118.10     33.84                36.0       2572.0           421.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17098      2681.0       980.0         7.1088            443300.0   \n",
       "6622        966.0       345.0         3.6094            261500.0   \n",
       "10016       782.0       344.0         3.7125            175000.0   \n",
       "14984       441.0       174.0         4.7679            169500.0   \n",
       "8018       1318.0       434.0         5.1836            219700.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17098      NEAR OCEAN          4  \n",
       "6622        <1H OCEAN          2  \n",
       "10016          INLAND          2  \n",
       "14984       <1H OCEAN          3  \n",
       "8018        <1H OCEAN          3  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据的数据和标签分开 \n",
    "houseing_labels = start_train_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "houseing = start_train_set.drop('median_house_value',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17098</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.46</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6841.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2681.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>7.1088</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.17</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1784.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>966.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>3.6094</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10016</th>\n",
       "      <td>-121.18</td>\n",
       "      <td>39.23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>3.7125</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>-116.99</td>\n",
       "      <td>32.72</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1112.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>4.7679</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8018</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.84</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2572.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>5.1836</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17098    -122.25     37.46                33.0       6841.0           950.0   \n",
       "6622     -118.13     34.17                52.0       1784.0           368.0   \n",
       "10016    -121.18     39.23                 8.0       2112.0           360.0   \n",
       "14984    -116.99     32.72                11.0       1112.0           164.0   \n",
       "8018     -118.10     33.84                36.0       2572.0           421.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "17098      2681.0       980.0         7.1088      NEAR OCEAN          4  \n",
       "6622        966.0       345.0         3.6094       <1H OCEAN          2  \n",
       "10016       782.0       344.0         3.7125          INLAND          2  \n",
       "14984       441.0       174.0         4.7679       <1H OCEAN          3  \n",
       "8018       1318.0       434.0         5.1836       <1H OCEAN          3  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17098 to 11669\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16347 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   ocean_proximity     16512 non-null  object  \n",
      " 9   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "houseing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗\n",
    "   ### 1.把缺失数据的特征 删除掉\n",
    "   ### 2.把特征保留，把存在问题的地方给删除掉\n",
    "   ### 3. 把存在问题的地方 补上值  补0或者补其他的元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14307</th>\n",
       "      <td>-117.14</td>\n",
       "      <td>32.71</td>\n",
       "      <td>52.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>480.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.8696</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17198</th>\n",
       "      <td>-119.75</td>\n",
       "      <td>34.45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2864.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>5.5073</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>-119.72</td>\n",
       "      <td>36.76</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6403.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3573.0</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>2.3006</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5236</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>33.94</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1417.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>2.3333</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18786</th>\n",
       "      <td>-122.42</td>\n",
       "      <td>40.44</td>\n",
       "      <td>16.0</td>\n",
       "      <td>994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>495.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>2.1875</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14307    -117.14     32.71                52.0        500.0             NaN   \n",
       "17198    -119.75     34.45                 6.0       2864.0             NaN   \n",
       "2115     -119.72     36.76                23.0       6403.0             NaN   \n",
       "5236     -118.23     33.94                36.0       1110.0             NaN   \n",
       "18786    -122.42     40.44                16.0        994.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "14307       480.0       108.0         1.8696      NEAR OCEAN          2  \n",
       "17198      1404.0       603.0         5.5073      NEAR OCEAN          3  \n",
       "2115       3573.0      1260.0         2.3006          INLAND          2  \n",
       "5236       1417.0       302.0         2.3333       <1H OCEAN          2  \n",
       "18786       495.0       181.0         2.1875          INLAND          2  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = houseing[houseing.isnull().any(axis=1)]\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 放弃缺失 的数据\n",
    "# rows.dropna(subset=['total_bedrooms'])\n",
    "# # 放弃这个属性\n",
    "# rows.drop('total_bedrooms',axis = 1)\n",
    "# 将缺失值 用中位数去替换\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer( strategy ='median')\n",
    "\n",
    "houseing_111 = houseing.drop('ocean_proximity',axis=1)\n",
    "imputer.fit(houseing_111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.18500e+02,  3.42600e+01,  2.90000e+01,  2.12700e+03,\n",
       "        4.36000e+02,  1.16600e+03,  4.10000e+02,  3.53645e+00,\n",
       "        2.00000e+00])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.5    ,   34.26   ,   29.     , 2127.     ,  436.     ,\n",
       "       1166.     ,  410.     ,    3.53645])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_111.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
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       "      <th>households</th>\n",
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       "      <th>income_cat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17098</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.46</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6841.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2681.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>7.1088</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.17</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1784.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>966.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>3.6094</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10016</th>\n",
       "      <td>-121.18</td>\n",
       "      <td>39.23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>3.7125</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>-116.99</td>\n",
       "      <td>32.72</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1112.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>4.7679</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8018</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.84</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2572.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>5.1836</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14743</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>32.58</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2879.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>3.5125</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>-120.87</td>\n",
       "      <td>38.37</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3998.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>1614.0</td>\n",
       "      <td>698.0</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2563</th>\n",
       "      <td>-124.16</td>\n",
       "      <td>40.80</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1703.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>1.1386</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>-122.02</td>\n",
       "      <td>37.95</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3526.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>5.6642</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11669</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.86</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2307.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.4306</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17098    -122.25     37.46                33.0       6841.0           950.0   \n",
       "6622     -118.13     34.17                52.0       1784.0           368.0   \n",
       "10016    -121.18     39.23                 8.0       2112.0           360.0   \n",
       "14984    -116.99     32.72                11.0       1112.0           164.0   \n",
       "8018     -118.10     33.84                36.0       2572.0           421.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "14743    -117.06     32.58                11.0       2879.0           679.0   \n",
       "1040     -120.87     38.37                28.0       3998.0           765.0   \n",
       "2563     -124.16     40.80                52.0       1703.0           500.0   \n",
       "1491     -122.02     37.95                22.0       3526.0           510.0   \n",
       "11669    -118.01     33.86                29.0       2307.0           452.0   \n",
       "\n",
       "       population  households  median_income income_cat  \n",
       "17098      2681.0       980.0         7.1088          4  \n",
       "6622        966.0       345.0         3.6094          2  \n",
       "10016       782.0       344.0         3.7125          2  \n",
       "14984       441.0       174.0         4.7679          3  \n",
       "8018       1318.0       434.0         5.1836          3  \n",
       "...           ...         ...            ...        ...  \n",
       "14743      2098.0       673.0         3.5125          2  \n",
       "1040       1614.0       698.0         2.8125          2  \n",
       "2563        952.0       435.0         1.1386          1  \n",
       "1491       1660.0       508.0         5.6642          3  \n",
       "11669      1218.0       402.0         3.4306          2  \n",
       "\n",
       "[16512 rows x 9 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_111"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(houseing_111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "houseing_tr = pd.DataFrame(X,columns= houseing_111.columns,index = houseing_111.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17098 to 11669\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           16512 non-null  float64\n",
      " 1   latitude            16512 non-null  float64\n",
      " 2   housing_median_age  16512 non-null  float64\n",
      " 3   total_rooms         16512 non-null  float64\n",
      " 4   total_bedrooms      16512 non-null  float64\n",
      " 5   population          16512 non-null  float64\n",
      " 6   households          16512 non-null  float64\n",
      " 7   median_income       16512 non-null  float64\n",
      " 8   income_cat          16512 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "houseing_tr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17098</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10016</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8018</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7655</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7395</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11758</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17098      NEAR OCEAN\n",
       "6622        <1H OCEAN\n",
       "10016          INLAND\n",
       "14984       <1H OCEAN\n",
       "8018        <1H OCEAN\n",
       "7655        <1H OCEAN\n",
       "693          NEAR BAY\n",
       "461          NEAR BAY\n",
       "7395        <1H OCEAN\n",
       "11758          INLAND"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_cat = houseing[['ocean_proximity']]\n",
    "houseing_cat.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ocean_proximity\n",
       "<1H OCEAN          7323\n",
       "INLAND             5281\n",
       "NEAR OCEAN         2096\n",
       "NEAR BAY           1810\n",
       "ISLAND                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing[['ocean_proximity']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 0, 1, ..., 4, 3, 0])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将文本转化成对应的数字分类，使用转换器\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "houseing_cat = houseing['ocean_proximity']\n",
    "houseing_2 = encoder.fit_transform(houseing_cat)\n",
    "houseing_2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4],\n",
       "       [0],\n",
       "       [1],\n",
       "       ...,\n",
       "       [4],\n",
       "       [3],\n",
       "       [0]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_2.reshape([-1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "houseing_cat_hot  =encoder.fit_transform(houseing_2.reshape([-1,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_cat_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [1., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从稀疏矩阵转换成numpy\n",
    "houseing_cat_hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # from sklearn.preprocessing import LabelBinarizer\n",
    "# # ecncode = LabelBinarizer()\n",
    "# # houseing_cat_onehot = encoder.fit_transform(houseing_cat)\n",
    "# # houseing_cat_onehot\n",
    "# houseing_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#自定义转换器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.base import BaseEstimator,TransformerMixin\n",
    "# rooms_ix,bedroom_ix,population_ix,household_ix = [list(houseing.columns).index(col) for col in (\"total_rooms\",\"total_bedrooms\",\"population\",\"households\")]\n",
    "# rooms_ix,bedroom_ix,population_ix,household_ix\n",
    "# class CombinedAttributesAdder(BaseEstimator,TransformerMixin):\n",
    "#     def __init__(self,add_population_per_households= True):\n",
    "#         self.add_population_per_households = add_population_per_households\n",
    "#     def fit(self,X,y=None):\n",
    "#         return self\n",
    "#     def transform(self,X,y=None):\n",
    "#         room_household = X[:,rooms_ix]/X[:,household_ix]\n",
    "#         bedrooms_per_rooms = X[:,bedroom_ix]/X[:,rooms_ix]\n",
    "#         if self.add_population_per_households:\n",
    "#             population_per_households = X[:,population_ix]/X[:,household_ix]\n",
    "#             return np.c_(X,room_household,bedrooms_per_rooms,population_per_households)\n",
    "#         else:\n",
    "#             return np.c_(X,room_household,bedrooms_per_rooms)\n",
    "# attr_add = CombinedAttributesAdder(add_population_per_households=False)\n",
    "# houseing_extr = attr_add.transform(houseing.values)\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = [list(houseing.columns).index(col) for col in (\"total_rooms\", \"total_bedrooms\", \"population\", \"households\")]\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix \n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(self, X, y = None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household,population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household,population_per_household]\n",
    "        \n",
    "        \n",
    "        \n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room = False)\n",
    "houseing_extr = attr_adder.transform(houseing.values)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-122.25, 37.46, 33.0, ..., 4, 6.9806122448979595,\n",
       "        2.7357142857142858],\n",
       "       [-118.13, 34.17, 52.0, ..., 2, 5.171014492753623, 2.8],\n",
       "       [-121.18, 39.23, 8.0, ..., 2, 6.1395348837209305,\n",
       "        2.2732558139534884],\n",
       "       ...,\n",
       "       [-124.16, 40.8, 52.0, ..., 1, 3.914942528735632,\n",
       "        2.188505747126437],\n",
       "       [-122.02, 37.95, 22.0, ..., 3, 6.940944881889764,\n",
       "        3.267716535433071],\n",
       "       [-118.01, 33.86, 29.0, ..., 2, 5.7388059701492535,\n",
       "        3.029850746268657]], dtype=object)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_extr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.46</td>\n",
       "      <td>33</td>\n",
       "      <td>6841</td>\n",
       "      <td>950</td>\n",
       "      <td>2681</td>\n",
       "      <td>980</td>\n",
       "      <td>7.1088</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>6.98061</td>\n",
       "      <td>2.73571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.17</td>\n",
       "      <td>52</td>\n",
       "      <td>1784</td>\n",
       "      <td>368</td>\n",
       "      <td>966</td>\n",
       "      <td>345</td>\n",
       "      <td>3.6094</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>5.17101</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-121.18</td>\n",
       "      <td>39.23</td>\n",
       "      <td>8</td>\n",
       "      <td>2112</td>\n",
       "      <td>360</td>\n",
       "      <td>782</td>\n",
       "      <td>344</td>\n",
       "      <td>3.7125</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>6.13953</td>\n",
       "      <td>2.27326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-116.99</td>\n",
       "      <td>32.72</td>\n",
       "      <td>11</td>\n",
       "      <td>1112</td>\n",
       "      <td>164</td>\n",
       "      <td>441</td>\n",
       "      <td>174</td>\n",
       "      <td>4.7679</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3908</td>\n",
       "      <td>2.53448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-118.1</td>\n",
       "      <td>33.84</td>\n",
       "      <td>36</td>\n",
       "      <td>2572</td>\n",
       "      <td>421</td>\n",
       "      <td>1318</td>\n",
       "      <td>434</td>\n",
       "      <td>5.1836</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.92627</td>\n",
       "      <td>3.03687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16507</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>32.58</td>\n",
       "      <td>11</td>\n",
       "      <td>2879</td>\n",
       "      <td>679</td>\n",
       "      <td>2098</td>\n",
       "      <td>673</td>\n",
       "      <td>3.5125</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.27786</td>\n",
       "      <td>3.11738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16508</th>\n",
       "      <td>-120.87</td>\n",
       "      <td>38.37</td>\n",
       "      <td>28</td>\n",
       "      <td>3998</td>\n",
       "      <td>765</td>\n",
       "      <td>1614</td>\n",
       "      <td>698</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.72779</td>\n",
       "      <td>2.31232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16509</th>\n",
       "      <td>-124.16</td>\n",
       "      <td>40.8</td>\n",
       "      <td>52</td>\n",
       "      <td>1703</td>\n",
       "      <td>500</td>\n",
       "      <td>952</td>\n",
       "      <td>435</td>\n",
       "      <td>1.1386</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.91494</td>\n",
       "      <td>2.18851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16510</th>\n",
       "      <td>-122.02</td>\n",
       "      <td>37.95</td>\n",
       "      <td>22</td>\n",
       "      <td>3526</td>\n",
       "      <td>510</td>\n",
       "      <td>1660</td>\n",
       "      <td>508</td>\n",
       "      <td>5.6642</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "      <td>6.94094</td>\n",
       "      <td>3.26772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16511</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.86</td>\n",
       "      <td>29</td>\n",
       "      <td>2307</td>\n",
       "      <td>452</td>\n",
       "      <td>1218</td>\n",
       "      <td>402</td>\n",
       "      <td>3.4306</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>5.73881</td>\n",
       "      <td>3.02985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1   2     3    4     5    6       7           8  9   \\\n",
       "0     -122.25  37.46  33  6841  950  2681  980  7.1088  NEAR OCEAN  4   \n",
       "1     -118.13  34.17  52  1784  368   966  345  3.6094   <1H OCEAN  2   \n",
       "2     -121.18  39.23   8  2112  360   782  344  3.7125      INLAND  2   \n",
       "3     -116.99  32.72  11  1112  164   441  174  4.7679   <1H OCEAN  3   \n",
       "4      -118.1  33.84  36  2572  421  1318  434  5.1836   <1H OCEAN  3   \n",
       "...       ...    ...  ..   ...  ...   ...  ...     ...         ... ..   \n",
       "16507 -117.06  32.58  11  2879  679  2098  673  3.5125  NEAR OCEAN  2   \n",
       "16508 -120.87  38.37  28  3998  765  1614  698  2.8125      INLAND  2   \n",
       "16509 -124.16   40.8  52  1703  500   952  435  1.1386  NEAR OCEAN  1   \n",
       "16510 -122.02  37.95  22  3526  510  1660  508  5.6642    NEAR BAY  3   \n",
       "16511 -118.01  33.86  29  2307  452  1218  402  3.4306   <1H OCEAN  2   \n",
       "\n",
       "            10       11  \n",
       "0      6.98061  2.73571  \n",
       "1      5.17101      2.8  \n",
       "2      6.13953  2.27326  \n",
       "3       6.3908  2.53448  \n",
       "4      5.92627  3.03687  \n",
       "...        ...      ...  \n",
       "16507  4.27786  3.11738  \n",
       "16508  5.72779  2.31232  \n",
       "16509  3.91494  2.18851  \n",
       "16510  6.94094  3.26772  \n",
       "16511  5.73881  3.02985  \n",
       "\n",
       "[16512 rows x 12 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_tr = pd.DataFrame(houseing_extr)\n",
    "houseing_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>111111111</th>\n",
       "      <th>22222222222</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17098</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.46</td>\n",
       "      <td>33</td>\n",
       "      <td>6841</td>\n",
       "      <td>950</td>\n",
       "      <td>2681</td>\n",
       "      <td>980</td>\n",
       "      <td>7.1088</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>6.98061</td>\n",
       "      <td>2.73571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.17</td>\n",
       "      <td>52</td>\n",
       "      <td>1784</td>\n",
       "      <td>368</td>\n",
       "      <td>966</td>\n",
       "      <td>345</td>\n",
       "      <td>3.6094</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>5.17101</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10016</th>\n",
       "      <td>-121.18</td>\n",
       "      <td>39.23</td>\n",
       "      <td>8</td>\n",
       "      <td>2112</td>\n",
       "      <td>360</td>\n",
       "      <td>782</td>\n",
       "      <td>344</td>\n",
       "      <td>3.7125</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>6.13953</td>\n",
       "      <td>2.27326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>-116.99</td>\n",
       "      <td>32.72</td>\n",
       "      <td>11</td>\n",
       "      <td>1112</td>\n",
       "      <td>164</td>\n",
       "      <td>441</td>\n",
       "      <td>174</td>\n",
       "      <td>4.7679</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3908</td>\n",
       "      <td>2.53448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8018</th>\n",
       "      <td>-118.1</td>\n",
       "      <td>33.84</td>\n",
       "      <td>36</td>\n",
       "      <td>2572</td>\n",
       "      <td>421</td>\n",
       "      <td>1318</td>\n",
       "      <td>434</td>\n",
       "      <td>5.1836</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.92627</td>\n",
       "      <td>3.03687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14743</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>32.58</td>\n",
       "      <td>11</td>\n",
       "      <td>2879</td>\n",
       "      <td>679</td>\n",
       "      <td>2098</td>\n",
       "      <td>673</td>\n",
       "      <td>3.5125</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.27786</td>\n",
       "      <td>3.11738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>-120.87</td>\n",
       "      <td>38.37</td>\n",
       "      <td>28</td>\n",
       "      <td>3998</td>\n",
       "      <td>765</td>\n",
       "      <td>1614</td>\n",
       "      <td>698</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.72779</td>\n",
       "      <td>2.31232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2563</th>\n",
       "      <td>-124.16</td>\n",
       "      <td>40.8</td>\n",
       "      <td>52</td>\n",
       "      <td>1703</td>\n",
       "      <td>500</td>\n",
       "      <td>952</td>\n",
       "      <td>435</td>\n",
       "      <td>1.1386</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.91494</td>\n",
       "      <td>2.18851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>-122.02</td>\n",
       "      <td>37.95</td>\n",
       "      <td>22</td>\n",
       "      <td>3526</td>\n",
       "      <td>510</td>\n",
       "      <td>1660</td>\n",
       "      <td>508</td>\n",
       "      <td>5.6642</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "      <td>6.94094</td>\n",
       "      <td>3.26772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11669</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.86</td>\n",
       "      <td>29</td>\n",
       "      <td>2307</td>\n",
       "      <td>452</td>\n",
       "      <td>1218</td>\n",
       "      <td>402</td>\n",
       "      <td>3.4306</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>5.73881</td>\n",
       "      <td>3.02985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "17098   -122.25    37.46                 33        6841            950   \n",
       "6622    -118.13    34.17                 52        1784            368   \n",
       "10016   -121.18    39.23                  8        2112            360   \n",
       "14984   -116.99    32.72                 11        1112            164   \n",
       "8018     -118.1    33.84                 36        2572            421   \n",
       "...         ...      ...                ...         ...            ...   \n",
       "14743   -117.06    32.58                 11        2879            679   \n",
       "1040    -120.87    38.37                 28        3998            765   \n",
       "2563    -124.16     40.8                 52        1703            500   \n",
       "1491    -122.02    37.95                 22        3526            510   \n",
       "11669   -118.01    33.86                 29        2307            452   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "17098       2681        980        7.1088      NEAR OCEAN          4   \n",
       "6622         966        345        3.6094       <1H OCEAN          2   \n",
       "10016        782        344        3.7125          INLAND          2   \n",
       "14984        441        174        4.7679       <1H OCEAN          3   \n",
       "8018        1318        434        5.1836       <1H OCEAN          3   \n",
       "...          ...        ...           ...             ...        ...   \n",
       "14743       2098        673        3.5125      NEAR OCEAN          2   \n",
       "1040        1614        698        2.8125          INLAND          2   \n",
       "2563         952        435        1.1386      NEAR OCEAN          1   \n",
       "1491        1660        508        5.6642        NEAR BAY          3   \n",
       "11669       1218        402        3.4306       <1H OCEAN          2   \n",
       "\n",
       "      111111111 22222222222  \n",
       "17098   6.98061     2.73571  \n",
       "6622    5.17101         2.8  \n",
       "10016   6.13953     2.27326  \n",
       "14984    6.3908     2.53448  \n",
       "8018    5.92627     3.03687  \n",
       "...         ...         ...  \n",
       "14743   4.27786     3.11738  \n",
       "1040    5.72779     2.31232  \n",
       "2563    3.91494     2.18851  \n",
       "1491    6.94094     3.26772  \n",
       "11669   5.73881     3.02985  \n",
       "\n",
       "[16512 rows x 12 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对houseing_tr 的行和列 进行定义\n",
    "\n",
    "houseing_tr = pd.DataFrame(\n",
    "    houseing_extr,\n",
    "    columns = list(houseing.columns)+['111111111','22222222222'],\n",
    "    index = houseing.index\n",
    ")\n",
    "houseing_tr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征缩放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最重要也是最需要应用在数据上的转换器，就是特征缩放，输入数值属性有很大的比例差异，会导致机器学习算法性能表现不佳\n",
    "# 房间总数 范围6到39320，收入中位数范围0到15\n",
    "# 目标值通常不需要缩放\n",
    "# 同比缩放所有属性，2种方法 最小-最大缩放和标准化\n",
    "# 最小-最大缩放，又叫归一化，将值重新缩放到0到1之间，将值减去最小值并除以最大值和最小值差，如果你不希望是0到1，可以调整超参数feature_range\n",
    "# 标准化 减去平均值 ，所以标准化均值总是0，然后除以方差，结果的分布具备单位方差，不同于归一化，标准化不将值绑定到特定范围，受异常值影响小\n",
    "# 缩放器仅用来拟合训练集，不是完成的数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 转换流水线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 许多数据转换的步骤需要以正确的顺序来执行， PipeLine来支持这样的转换\n",
    "# pipline构造函数会通过一系列名称/估算器的配对来定义步骤的序列，必须是转换器，必须有fit_fransform()方法\n",
    "# 调用流水芡的fit方法时，会在所有转换器上按照顺序依次调用fit_transform()，将一个调用的输出作为参数传递给下一个调用方法，直到传递到最终\n",
    "# 估算器，只会调用fit方法\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler())\n",
    "    ])\n",
    "\n",
    "houseing_num_tr = num_pipeline.fit_transform(houseing_111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17098 to 11669\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16347 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "houseing_111.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataFrame -> series -> ndarray\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs =list(houseing_111) \n",
    "num_attribs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(num_attribs)),\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin \n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self, x, y=0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),               \n",
    "        ('LabelBinarizer', MyLabelBinarizer()),\n",
    "    ])\n",
    "\n",
    "# housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipline\", num_pipeline,),\n",
    "        ('cat_pipline', cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.33785306,  0.85274886,  0.3489932 , ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [ 0.72025517, -0.68736832,  1.85968555, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-0.80334437,  1.68132254, -1.63875989, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       ...,\n",
       "       [-2.29197605,  2.41627208,  1.85968555, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [-1.22295867,  1.08212802, -0.52561816, ...,  0.        ,\n",
       "         1.        ,  0.        ],\n",
       "       [ 0.78020007, -0.83248574,  0.03095271, ...,  0.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(houseing)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.337853</td>\n",
       "      <td>0.852749</td>\n",
       "      <td>0.348993</td>\n",
       "      <td>1.895556</td>\n",
       "      <td>0.971152</td>\n",
       "      <td>1.078619</td>\n",
       "      <td>1.235631</td>\n",
       "      <td>1.705080</td>\n",
       "      <td>2.223903</td>\n",
       "      <td>0.604059</td>\n",
       "      <td>-0.058217</td>\n",
       "      <td>-1.150102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.720255</td>\n",
       "      <td>-0.687368</td>\n",
       "      <td>1.859686</td>\n",
       "      <td>-0.387739</td>\n",
       "      <td>-0.400523</td>\n",
       "      <td>-0.400967</td>\n",
       "      <td>-0.402408</td>\n",
       "      <td>-0.136615</td>\n",
       "      <td>-0.496883</td>\n",
       "      <td>-0.103280</td>\n",
       "      <td>-0.043813</td>\n",
       "      <td>-0.111548</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.803344</td>\n",
       "      <td>1.681323</td>\n",
       "      <td>-1.638760</td>\n",
       "      <td>-0.239643</td>\n",
       "      <td>-0.419377</td>\n",
       "      <td>-0.559710</td>\n",
       "      <td>-0.404988</td>\n",
       "      <td>-0.082355</td>\n",
       "      <td>-0.496883</td>\n",
       "      <td>0.275297</td>\n",
       "      <td>-0.161834</td>\n",
       "      <td>-0.663468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.289732</td>\n",
       "      <td>-1.366143</td>\n",
       "      <td>-1.400230</td>\n",
       "      <td>-0.691155</td>\n",
       "      <td>-0.881316</td>\n",
       "      <td>-0.853902</td>\n",
       "      <td>-0.843518</td>\n",
       "      <td>0.473090</td>\n",
       "      <td>0.863510</td>\n",
       "      <td>0.373514</td>\n",
       "      <td>-0.103304</td>\n",
       "      <td>-1.017398</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.735241</td>\n",
       "      <td>-0.841848</td>\n",
       "      <td>0.587524</td>\n",
       "      <td>-0.031948</td>\n",
       "      <td>-0.275611</td>\n",
       "      <td>-0.097285</td>\n",
       "      <td>-0.172825</td>\n",
       "      <td>0.691869</td>\n",
       "      <td>0.863510</td>\n",
       "      <td>0.191934</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>-0.767751</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0 -1.337853  0.852749  0.348993  1.895556  0.971152  1.078619  1.235631   \n",
       "1  0.720255 -0.687368  1.859686 -0.387739 -0.400523 -0.400967 -0.402408   \n",
       "2 -0.803344  1.681323 -1.638760 -0.239643 -0.419377 -0.559710 -0.404988   \n",
       "3  1.289732 -1.366143 -1.400230 -0.691155 -0.881316 -0.853902 -0.843518   \n",
       "4  0.735241 -0.841848  0.587524 -0.031948 -0.275611 -0.097285 -0.172825   \n",
       "\n",
       "         7         8         9         10        11   12   13   14   15   16  \n",
       "0  1.705080  2.223903  0.604059 -0.058217 -1.150102  0.0  0.0  0.0  0.0  1.0  \n",
       "1 -0.136615 -0.496883 -0.103280 -0.043813 -0.111548  1.0  0.0  0.0  0.0  0.0  \n",
       "2 -0.082355 -0.496883  0.275297 -0.161834 -0.663468  0.0  1.0  0.0  0.0  0.0  \n",
       "3  0.473090  0.863510  0.373514 -0.103304 -1.017398  1.0  0.0  0.0  0.0  0.0  \n",
       "4  0.691869  0.863510  0.191934  0.009259 -0.767751  1.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_prepared = pd.DataFrame(housing_prepared)\n",
    "houseing_prepared.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17098 to 11669\n",
      "Data columns (total 1 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   median_house_value  16512 non-null  float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 258.0 KB\n"
     ]
    }
   ],
   "source": [
    "# df = pd.DataFrame(houseing_prepared)\n",
    "# df.info()\n",
    "# houseing_labels\n",
    "dp = pd.DataFrame(houseing_labels)\n",
    "dp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  训练模型和评估训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_ryg=LinearRegression()\n",
    "lin_ryg.fit(houseing_prepared,houseing_labels)   #fit函数中X，y 的值分别为数据和标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [392151.65166671 223801.02404582  95912.83941402 233218.59425031\n",
      " 276468.975285  ]\n"
     ]
    }
   ],
   "source": [
    "# 预测数据\n",
    "some_data = houseing.iloc[:5]\n",
    "some_labels = houseing_labels.iloc[:5]\n",
    "\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print(\"Predictions:\", lin_ryg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68124.87518086718"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "houseing_predictions = lin_ryg.predict(houseing_prepared)\n",
    "lin_mse = mean_squared_error(houseing_labels, houseing_predictions)\n",
    "lin_mse\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [392151.65166671 223801.02404582  95912.83941402 233218.59425031\n",
      " 276468.975285  ]\n"
     ]
    }
   ],
   "source": [
    "some_data = houseing.iloc[:5]\n",
    "some_labels = houseing_labels.iloc[:5]\n",
    "\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print(\"Predictions:\", lin_ryg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=42)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg.fit(houseing_prepared, houseing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(houseing_prepared)\n",
    "tree_mse = mean_squared_error(houseing_labels, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型调参和网格搜索\n",
    "   1. 手动调整超参数，找到很好的组合很困难\n",
    "   2. 使用GridSearchCV替你进行搜索，告诉它，进行试验的超参数是什么，和需要尝试的值，它会使用交叉验证评估所有超参数的可能组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(houseing_prepared, houseing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features=6, n_estimators=30, random_state=42)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63727.72269427062 {'max_features': 2, 'n_estimators': 3}\n",
      "55280.21755281769 {'max_features': 2, 'n_estimators': 10}\n",
      "52888.53727271681 {'max_features': 2, 'n_estimators': 30}\n",
      "61474.81042572098 {'max_features': 4, 'n_estimators': 3}\n",
      "53521.52022952455 {'max_features': 4, 'n_estimators': 10}\n",
      "51276.22591016041 {'max_features': 4, 'n_estimators': 30}\n",
      "59790.49915299937 {'max_features': 6, 'n_estimators': 3}\n",
      "52612.06361218446 {'max_features': 6, 'n_estimators': 10}\n",
      "50468.71496875775 {'max_features': 6, 'n_estimators': 30}\n",
      "60008.99345085311 {'max_features': 8, 'n_estimators': 3}\n",
      "53016.887173166375 {'max_features': 8, 'n_estimators': 10}\n",
      "50858.914492840355 {'max_features': 8, 'n_estimators': 30}\n",
      "62326.84680034537 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54629.45402769521 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "60466.69405824235 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "53096.96552039283 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "59055.18147914716 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "52142.77049120967 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001C2993AE730>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001C29D3787C0>},\n",
       "                   random_state=42, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=5),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(houseing_prepared, houseing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50564.36525264502 {'max_features': 3, 'n_estimators': 180}\n",
      "57095.52250545637 {'max_features': 1, 'n_estimators': 15}\n",
      "50986.84937164269 {'max_features': 3, 'n_estimators': 72}\n",
      "56325.97857209889 {'max_features': 1, 'n_estimators': 21}\n",
      "50743.235214596934 {'max_features': 3, 'n_estimators': 122}\n",
      "50959.29837747602 {'max_features': 3, 'n_estimators': 75}\n",
      "50857.96325912074 {'max_features': 3, 'n_estimators': 88}\n",
      "54374.262711707015 {'max_features': 1, 'n_estimators': 100}\n",
      "50246.893059729766 {'max_features': 4, 'n_estimators': 152}\n",
      "50622.60839836937 {'max_features': 3, 'n_estimators': 150}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.83654094e-02, 6.43697319e-02, 4.29281626e-02, 1.74158476e-02,\n",
       "       1.76156364e-02, 1.74586656e-02, 1.55194360e-02, 2.59655732e-01,\n",
       "       1.35850099e-01, 4.60801057e-02, 1.08462345e-01, 6.23038373e-02,\n",
       "       1.60903376e-02, 1.20940817e-01, 1.40675098e-04, 2.57560514e-03,\n",
       "       4.22755726e-03])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.25965573233641887, 'median_income'),\n",
       " (0.13585009908235285, 'income_cat'),\n",
       " (0.12094081653003608, 'INLAND'),\n",
       " (0.10846234454055852, 'population_per_household'),\n",
       " (0.06836540935447641, 'longitude'),\n",
       " (0.06436973188174248, 'latitude'),\n",
       " (0.062303837309372695, 'bedrooms_per_room'),\n",
       " (0.04608010566473926, 'rooms_per_household'),\n",
       " (0.04292816262817665, 'housing_median_age'),\n",
       " (0.01761563643277725, 'total_bedrooms'),\n",
       " (0.017458665586836078, 'population'),\n",
       " (0.017415847583308754, 'total_rooms'),\n",
       " (0.01609033759955043, '<1H OCEAN'),\n",
       " (0.015519435974335657, 'households'),\n",
       " (0.004227557260928378, 'NEAR OCEAN'),\n",
       " (0.0025756051363129895, 'NEAR BAY'),\n",
       " (0.00014067509807673387, 'ISLAND')]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# num_attribs\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "houseing_cat = houseing['ocean_proximity']\n",
    "houseing_cat_encoder = encoder.fit_transform(houseing_cat)\n",
    "houseing_cat_encoder\n",
    "extra_attribs = [\"rooms_per_household\", \"population_per_household\", \"bedrooms_per_room\"]\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['0.00014067509807673387' 'ISLAND']\n",
      " ['0.0025756051363129895' 'NEAR BAY']\n",
      " ['0.004227557260928378' 'NEAR OCEAN']\n",
      " ['0.015519435974335657' 'households']\n",
      " ['0.01609033759955043' '<1H OCEAN']\n",
      " ['0.017415847583308754' 'total_rooms']\n",
      " ['0.017458665586836078' 'population']\n",
      " ['0.01761563643277725' 'total_bedrooms']\n",
      " ['0.04292816262817665' 'housing_median_age']\n",
      " ['0.04608010566473926' 'rooms_per_household']\n",
      " ['0.062303837309372695' 'bedrooms_per_room']\n",
      " ['0.06436973188174248' 'latitude']\n",
      " ['0.06836540935447641' 'longitude']\n",
      " ['0.10846234454055852' 'population_per_household']\n",
      " ['0.12094081653003608' 'INLAND']\n",
      " ['0.13585009908235285' 'income_cat']\n",
      " ['0.25965573233641887' 'median_income']]\n"
     ]
    }
   ],
   "source": [
    "ba = sorted(zip(feature_importances, attributes))\n",
    "print(np.array(ba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "def indices_of_top_k(arr, k):\n",
    "    return np.sort(np.argpartition(np.array(arr), -k)[-k:])\n",
    "\n",
    "class TopFeatureSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, feature_importances, k):\n",
    "        self.feature_importances = feature_importances\n",
    "        self.k = k\n",
    "    def fit(self, X, y = None):\n",
    "        self.feature_indices_ = indices_of_top_k(self.feature_importances, self.k)\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[:, self.feature_indices_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.83654094e-02, 6.43697319e-02, 4.29281626e-02, 1.74158476e-02,\n",
       "       1.76156364e-02, 1.74586656e-02, 1.55194360e-02, 2.59655732e-01,\n",
       "       1.35850099e-01, 4.60801057e-02, 1.08462345e-01, 6.23038373e-02,\n",
       "       1.60903376e-02, 1.20940817e-01, 1.40675098e-04, 2.57560514e-03,\n",
       "       4.22755726e-03])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat',\n",
       " 'rooms_per_household',\n",
       " 'population_per_household',\n",
       " 'bedrooms_per_room',\n",
       " '<1H OCEAN',\n",
       " 'INLAND',\n",
       " 'ISLAND',\n",
       " 'NEAR BAY',\n",
       " 'NEAR OCEAN']"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  7,  8, 10, 13], dtype=int64)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 5\n",
    "# 按照最佳模型返回的数据进行排序\n",
    "top_k_feature_indices = indices_of_top_k(feature_importances, k)\n",
    "top_k_feature_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['longitude', 'median_income', 'income_cat',\n",
       "       'population_per_household', 'INLAND'], dtype='<U24')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(attributes)[top_k_feature_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0.25965573233641887', 'median_income'],\n",
       "       ['0.13585009908235285', 'income_cat'],\n",
       "       ['0.12094081653003608', 'INLAND'],\n",
       "       ['0.10846234454055852', 'population_per_household'],\n",
       "       ['0.06836540935447641', 'longitude']], dtype='<U32')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bba = sorted(zip(feature_importances, attributes), reverse=True)[:k]\n",
    "bba_np = np.array(bba)\n",
    "bba_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "preparation_and_feature_selection_pipeline = Pipeline([\n",
    "    # 独热和估算的总流水线\n",
    "    ('preparation', full_pipeline),\n",
    "    # 排序\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, k))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.33785306,  1.70508038,  2.2239029 , -0.05821693,  0.        ],\n",
       "       [ 0.72025517, -0.13661531, -0.49688283, -0.04381321,  0.        ],\n",
       "       [-0.80334437, -0.08235491, -0.49688283, -0.16183439,  1.        ]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_prepared_top_k_features = preparation_and_feature_selection_pipeline.fit_transform(houseing)\n",
    "houseing_prepared_top_k_features[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>population_per_household</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.337853</td>\n",
       "      <td>1.705080</td>\n",
       "      <td>2.223903</td>\n",
       "      <td>-0.058217</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.720255</td>\n",
       "      <td>-0.136615</td>\n",
       "      <td>-0.496883</td>\n",
       "      <td>-0.043813</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.803344</td>\n",
       "      <td>-0.082355</td>\n",
       "      <td>-0.496883</td>\n",
       "      <td>-0.161834</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.289732</td>\n",
       "      <td>0.473090</td>\n",
       "      <td>0.863510</td>\n",
       "      <td>-0.103304</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.735241</td>\n",
       "      <td>0.691869</td>\n",
       "      <td>0.863510</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   median_income  income_cat    INLAND  population_per_household  longitude\n",
       "0      -1.337853    1.705080  2.223903                 -0.058217        0.0\n",
       "1       0.720255   -0.136615 -0.496883                 -0.043813        0.0\n",
       "2      -0.803344   -0.082355 -0.496883                 -0.161834        1.0\n",
       "3       1.289732    0.473090  0.863510                 -0.103304        0.0\n",
       "4       0.735241    0.691869  0.863510                  0.009259        0.0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houseing_prepared = pd.DataFrame(houseing_prepared_top_k_features, columns = bba_np[:,1])\n",
    "houseing_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第三题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 流水线\n",
    "prepare_select_and_predict_pipeline = Pipeline([\n",
    "    # 独热流水线和估算器流水线\n",
    "    ('preparation', full_pipeline),\n",
    "    # 排序\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, k)),\n",
    "    # 随机森林 \n",
    "    ('final_model', RandomForestRegressor(**rnd_search.best_params_))\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preparation',\n",
       "                 FeatureUnion(transformer_list=[('num_pipline',\n",
       "                                                 Pipeline(steps=[('selector',\n",
       "                                                                  DataFrameSelector(attribute_names=['longitude',\n",
       "                                                                                                     'latitude',\n",
       "                                                                                                     'housing_median_age',\n",
       "                                                                                                     'total_rooms',\n",
       "                                                                                                     'total_bedrooms',\n",
       "                                                                                                     'population',\n",
       "                                                                                                     'households',\n",
       "                                                                                                     'median_income',\n",
       "                                                                                                     'income_cat'])),\n",
       "                                                                 ('imputer',\n",
       "                                                                  SimpleImputer(strategy='median')),\n",
       "                                                                 ('attribs_adder',\n",
       "                                                                  CombinedAttribu...\n",
       "                 TopFeatureSelector(feature_importances=array([6.83654094e-02, 6.43697319e-02, 4.29281626e-02, 1.74158476e-02,\n",
       "       1.76156364e-02, 1.74586656e-02, 1.55194360e-02, 2.59655732e-01,\n",
       "       1.35850099e-01, 4.60801057e-02, 1.08462345e-01, 6.23038373e-02,\n",
       "       1.60903376e-02, 1.20940817e-01, 1.40675098e-04, 2.57560514e-03,\n",
       "       4.22755726e-03]),\n",
       "                                    k=5)),\n",
       "                ('final_model',\n",
       "                 RandomForestRegressor(max_features=4, n_estimators=152))])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 网格搜索\n",
    "prepare_select_and_predict_pipeline.fit(houseing, houseing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值:\t [432787.61842105 246388.15789474 172462.5        180336.84210526]\n",
      "实际值:\t [443300.0, 261500.0, 175000.0, 169500.0]\n"
     ]
    }
   ],
   "source": [
    "# 从完整数据集中去前4个数据和标签, 数据用于完整\n",
    "some_data = houseing.iloc[:4]\n",
    "some_labels = houseing_labels.iloc[:4]\n",
    "\n",
    "print(\"预测值:\\t\", prepare_select_and_predict_pipeline.predict(some_data))\n",
    "print(\"实际值:\\t\", list(some_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [{\n",
    "      'preparation__num_pipline__attribs_adder__add_bedrooms_per_room':[True, False]\n",
    "}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_prep = GridSearchCV(prepare_select_and_predict_pipeline, param_grid, cv=5,\n",
    "                                scoring='neg_mean_squared_error', verbose=2, n_jobs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  10 out of  10 | elapsed:   38.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('preparation',\n",
       "                                        FeatureUnion(transformer_list=[('num_pipline',\n",
       "                                                                        Pipeline(steps=[('selector',\n",
       "                                                                                         DataFrameSelector(attribute_names=['longitude',\n",
       "                                                                                                                            'latitude',\n",
       "                                                                                                                            'housing_median_age',\n",
       "                                                                                                                            'total_rooms',\n",
       "                                                                                                                            'total_bedrooms',\n",
       "                                                                                                                            'population',\n",
       "                                                                                                                            'households',\n",
       "                                                                                                                            'median_income',\n",
       "                                                                                                                            'income_cat'])),\n",
       "                                                                                        ('imputer',\n",
       "                                                                                         SimpleImputer(strategy='median')),\n",
       "                                                                                        ('at...\n",
       "       1.35850099e-01, 4.60801057e-02, 1.08462345e-01, 6.23038373e-02,\n",
       "       1.60903376e-02, 1.20940817e-01, 1.40675098e-04, 2.57560514e-03,\n",
       "       4.22755726e-03]),\n",
       "                                                           k=5)),\n",
       "                                       ('final_model',\n",
       "                                        RandomForestRegressor(max_features=4,\n",
       "                                                              n_estimators=152))]),\n",
       "             n_jobs=2,\n",
       "             param_grid=[{'preparation__num_pipline__attribs_adder__add_bedrooms_per_room': [True,\n",
       "                                                                                             False]}],\n",
       "             scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_prep.fit(houseing, houseing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'preparation__num_pipline__attribs_adder__add_bedrooms_per_room': True}"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_prep.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
